def run_backtest(context):
n_bkt, args, mparams = context
formatter = logging.Formatter('%(asctime)s - n_bkt=' + str(n_bkt) + ' - %(levelname)s: %(message)s')
configure_root_logger(args.logtty, args.logfile,
MongoClient(args.host, args.port)[args.db][args.logmongo] if args.logmongo is not None
else None, formatter=formatter)
db = MongoClient(args.host, args.port)[args.db]
where_clause = defaultdict(lambda: {})
country, start_date, end_date = 'GB', parse_date(args.start), parse_date(args.end)
if start_date is not None:
where_clause['scheduled_off']['$gte'] = start_date
if end_date is not None:
where_clause['scheduled_off']['$lte'] = end_date
if country is not None:
where_clause['country'] = country
sorted_races = db[args.train].find(where_clause, sort=[('scheduled_off', 1)], timeout=False)
exec_services = HistoricalExecutionService(db)
strat = strategy.Balius(mu=mparams['mu'], sigma=mparams['sigma'], beta=mparams['beta'], tau=mparams['tau'],
draw_probability=mparams['draw_prob'], risk_aversion=mparams['risk_aversion'],
min_races=mparams['min_races'], max_exposure=mparams['max_exposure'])
st = time.clock()
strategy.backtest(exec_services, strat, sorted_races)
en = time.clock()
logging.info('Backtest finished in %.2f seconds' % (en - st))
strat_dict = strat.to_dict()
strat_id = db[STRATEGIES_COLL].insert(strat_dict)
logging.info('Strategy serialised to %s with id=%s' % (db[STRATEGIES_COLL], strat_id))
bets = price_historical_bets(db, exec_services.get_mu_bets()[0])
scorecard = make_scorecard(bets)
now = datetime.datetime.utcnow()
scorecard.update({'params': {'ts': strat_dict['hm']['ts'], 'risk': strat_dict['risk']},
'timestamp': now,
'run_seconds': en - st,
'strategy_id': strat_id})
scorecard_id = db[SCORECARDS_COLL].insert(scorecard)
logging.info('Scorecard inserted in %s with id=%s' % (db[SCORECARDS_COLL], scorecard_id))
db[BETS_COLL].insert(add_scorecard_id_to_dicts(scorecard_id, bets))
logging.info('Associated bets inserted in %s' % db[BETS_COLL])
markets = market_breakdown(bets).reset_index()
markets = pandas_to_dicts(markets, {'n_runners': int})
db[MARKETS_COLL].insert(add_scorecard_id_to_dicts(scorecard_id, markets))
logging.info('Associated market breakdown inserted in %s' % db[MARKETS_COLL])
def main(args):
db = MongoClient(args.host, args.port)[args.db]
now = datetime.datetime.utcnow()
markets = list(islice(get_future_markets(hours=args.hours), 0, 1))
logging.info('Found %d horse races in the next %d hours' % (len(markets), args.hours))
strats = get_traded_strategies(db[args.paper_trading], True)
for s in strats:
strat = Balius.from_dict(db[BKT_STRATEGIES].find_one({'_id': s['strategy_id']}))
strat.max_expsoure = 1e6
ex = PaperExecutionService()
backtest(ex, strat, markets)
bets = ex.get_mu_bets()[0]
if len(bets) == 0:
logging.info('Strategy with id=%s did not place any bets.' % s['strategy_id'])
continue
for bet in bets:
bet['strategy_id'] = s['strategy_id']
bet['timestamp'] = now
db[args.paper_bets].insert(bets)
logging.info('Successfully insert %d bets for strategy with id=%s' % (len(bets), s['strategy_id']))
def run_backtest(context):
n_bkt, args, mparams = context
formatter = logging.Formatter('%(asctime)s - n_bkt=' + str(n_bkt) +
' - %(levelname)s: %(message)s')
configure_root_logger(
args.logtty,
args.logfile,
MongoClient(args.host, args.port)[args.db][args.logmongo]
if args.logmongo is not None else None,
formatter=formatter)
db = MongoClient(args.host, args.port)[args.db]
where_clause = defaultdict(lambda: {})
country, start_date, end_date = 'GB', parse_date(args.start), parse_date(
args.end)
if start_date is not None:
where_clause['scheduled_off']['$gte'] = start_date
if end_date is not None:
where_clause['scheduled_off']['$lte'] = end_date
if country is not None:
where_clause['country'] = country
sorted_races = db[args.train].find(where_clause,
sort=[('scheduled_off', 1)],
timeout=False)
exec_services = HistoricalExecutionService(db)
strat = strategy.Balius(mu=mparams['mu'],
sigma=mparams['sigma'],
beta=mparams['beta'],
tau=mparams['tau'],
draw_probability=mparams['draw_prob'],
risk_aversion=mparams['risk_aversion'],
min_races=mparams['min_races'],
max_exposure=mparams['max_exposure'])
st = time.clock()
strategy.backtest(exec_services, strat, sorted_races)
en = time.clock()
logging.info('Backtest finished in %.2f seconds' % (en - st))
strat_dict = strat.to_dict()
strat_id = db[STRATEGIES_COLL].insert(strat_dict)
logging.info('Strategy serialised to %s with id=%s' %
(db[STRATEGIES_COLL], strat_id))
bets = price_historical_bets(db, exec_services.get_mu_bets()[0])
scorecard = make_scorecard(bets)
now = datetime.datetime.utcnow()
scorecard.update({
'params': {
'ts': strat_dict['hm']['ts'],
'risk': strat_dict['risk']
},
'timestamp': now,
'run_seconds': en - st,
'strategy_id': strat_id
})
scorecard_id = db[SCORECARDS_COLL].insert(scorecard)
logging.info('Scorecard inserted in %s with id=%s' %
(db[SCORECARDS_COLL], scorecard_id))
db[BETS_COLL].insert(add_scorecard_id_to_dicts(scorecard_id, bets))
logging.info('Associated bets inserted in %s' % db[BETS_COLL])
markets = market_breakdown(bets).reset_index()
markets = pandas_to_dicts(markets, {'n_runners': int})
db[MARKETS_COLL].insert(add_scorecard_id_to_dicts(scorecard_id, markets))
logging.info('Associated market breakdown inserted in %s' %
db[MARKETS_COLL])
